本研究之目的是探討重鬱症病患生活品質現況及其相關因素影響,係為一橫斷式研究,採立意取樣(Purposive Sampling)方式,樣本來自於台北市某醫學中心之精神部門診病患,共收案120位,以結構式問卷收集資料,本研究使用的工具包括:一、人口學特性;二、簡式健康量表;三、憂鬱程度量表;四、社會支持行為量表;五、台灣簡明版世界衛生組織生活品質問卷。資料分析以SPSS(Statistical Package for the Social Science)10.0版電腦軟體進行描述性分析,包括:次數分配、平均值(mean)、標準差(standard deviation)及百分比(﹪)。以獨立樣本t檢定(t-test)、皮爾森積差相關係數(Pearson’s product-moment correlation)、單因子變異數分析(one way ANOVA)及多重性逐步迴歸分析(stepwise multiple regression)等方法檢定人口學特性、憂鬱程度、社會支持與生活品質之相關性。經研究結果發現: 一、重鬱症病患之生活品質現況,以各範疇總分在4-20分之間,於生理健康範疇方面:平均值總分是11.54;於心理範疇方面:平均值總分是10.46;於社會關係範疇方面:平均值總分是11.61;於環境範疇方面;平均值總分是12.82,其結果是以『環境範疇』得分最高,感到最有品質,而以『心理範疇』得分最低,感到品質最差。 二、重鬱症病患人口學特性、憂鬱程度、社會支持與生活品質之相關性結果是: (一)生理健康範疇:有顯著線性相關的自變項有:年齡、第一次發病年齡、職業型態、居住狀況、經濟狀況、健康狀況、憂鬱程度、情緒支持。 (二)心理範疇:有顯著線性相關的自變項有:年齡、第一次發病年齡、職業型態、居住狀況、經濟狀況、健康狀況、憂鬱程度、社會支持總分、情緒支持、實質支持、訊息及價值觀支持。 (三)社會關係範疇:有顯著線性相關的自變項有:年齡、第一次發病年齡、婚姻狀況、職業型態、經濟狀況、健康狀況、憂鬱程度、社會支持總分、情緒支持、實質支持、訊息及價值觀支持。 (四)環境範疇:有顯著線性相關的自變項有:年齡、第一次發病年齡、婚姻狀況、經濟狀況、憂鬱程度、社會支持總分、情緒支持。 三、重鬱症病患之生活品質最佳預測逐步迴歸模式分析結果如下: (一)生理健康範疇方面:最佳預測因子是憂鬱程度,其可解釋總變異量是60.30﹪。 (二)心理範疇方面:最佳預測因子是憂鬱程度、社會支持總分、第一次發病年齡及年齡的大於等於71歲,其可解釋總變異量是76.50﹪。 (三)社會關係範疇方面:最佳預測因子是憂鬱程度、第一次發病年齡、社會支持的訊息及價值觀支持、職業型態的無業者及年齡的大於等於71歲,其可解釋總變異量是53.80﹪。 (四)環境範疇方面:最佳預測因子是憂鬱程度、經濟狀況的小於10萬、經濟狀況的大於等於11萬及年齡的51-70歲,其可解釋總變異量是53.50﹪。 本研究結果有助於瞭解重鬱症病患之生活品質狀況,同時可提供未來相關研究、發展本土性資料及衛生行政之參考依據。
The purpose of this study was to explore the quality of life and its related factors of patients with major depressive disorder. This study adopted cross-sectional research design. Purposive sampling was used to collect data from 120 patients came from the psychiatric outpatient departments of a medical center and a psychiatric hospital in northern Taiwan. Data were collected through structured questionnaires, which included:(1) demographic data sheet, (2) 5-item Brief Symptom Rating Scale (BSRS-5), (3) the Center For Epidemiologic Studies Depression Scale(CES-D), (4) Inventory of Socially Supportive Behaviors(ISSB), (5) World Health Organization’s Questionnaire on Quality of Life:BREF-Taiwan Version 100(WHOQOL-BREF). Data were analyzed by using SPSS/Windows 10.0 statistical software. The descriptive statistics methods included frequency distribution, arithmetic mean, standard deviation, and percentage. Data were analyzed by using independent t-test, Pearson’s product-moment correlation, one way ANOVA and stepwise multiple regression and so on, with which we can examine the demographic, degree of depression, social support and their links to the quality of life. Through the research, we found out the following results: 1.The condition of major depressive disorder patients’ quality of life is score range of each subtitle from 4-20. A sum from the following subtitles:physical domain is 11.54, psychological domain is 10.46, social domain is 11.61 and environmental domain is 12.82. The quality of life results is higher of environmental domain, which is poorer of psychological domain. 2.The results of quality of life related to demographic, degree of depression, social support were as followed: (1)Physical domain:the independent variables of significant linear correlation is age, the age of early-onset, type of occupation, living arrangements, economic status, health status, the degree of depression and emotional support. (2)Psychological domain:the independent variables of significant linear correlation is age, the age of early-onset, type of occupation, living arrangements, economic status, health status, the degree of depression, the sum of social support, emotional support, instrumental support, informational and appraisal support. (3)Social domain:the independent variables of significant linear correlation is age, the age of early-onset, marital status, type of occupation, economic status, health status, the degree of depression, the sum of social support, emotional support, instrumental support, informational and appraisal support. (4)Environmental domain:the independent variables of significant linear correlation is age, the age of early-onset, marital status, economic status, the degree of depression, the sum of social support and emotional support. 3.The best stepwise regression models of predicting major depressive disorder patients’ quality of life were as followed: (1)Physical domain:the best predicting factor is the degree of depression, and accounted for explained 60.30﹪of the variance. (2)Psychological domain:the best predicting factors are the degree of depression, the sum of social support, the age of early-onset, age more than 71 years old, and accounted for explained 76.50﹪of the variance. (3)Social domain:the best predicting factors are the degree of depression, the age of early-onset, informational and appraisal support, unemployment and age more than 71 years old, and accounted for explained 53.80﹪of the variance. (4)Environmental domain:the best predicting factors are the degree of depression, income less than one hundred thousand, income more than one hundred ten thousand and more than 71 years old, and accounted for explained 53.50﹪of the variance. The results of this study could understand major depressive disorder patients’ quality of life condition and provide related research in the future, develop local data and health administration reference of foundation.